Behaviors-based User Profiling and Classification-based Content Rating for Personalized Digital TV

This paper proposes a system embedded within digital TVs that aims at TV program recommendation based on descriptive metadata collected from versatile sources. The proposed system comprises a user profiling subsystem identifying user preferences and a user agent subsystem performing content rating....

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Bibliographic Details
Main Authors: Hyoseop Shin, Na Yeon Kim, Enu Yi Kim, Minsoo Lee
Format: Conference Proceeding
Language:English
Subjects:
Collaboration
Computer science
Digital TV
Hardware
Home appliances
Linear regression
Merging
Multimedia systems
Paper technology
Web and internet services
Online Access:Request full text
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Description
Summary:This paper proposes a system embedded within digital TVs that aims at TV program recommendation based on descriptive metadata collected from versatile sources. The proposed system comprises a user profiling subsystem identifying user preferences and a user agent subsystem performing content rating. For intelligent implicit TV profiling, a novel scheme for observable TV user behaviors is developed based on linear regression. Furthermore, a new relation-based similarity measure is suggested to improve categorized TV program rating precision. The experimental results show that the content rating precision is enhanced enough by the proposed schemes.
ISSN:2158-3994
2158-4001
DOI:10.1109/ICCE.2008.4588111